Abstract Alcohol initiation at an early age is associated with numerous negative outcomes, including a significant increase in the risk of developing an alcohol-use disorder later in life. Vulnerability for early misuse and other problematic alcohol use behaviors have been linked to individual differences in brain function. However, few studies have sought to identify brain-based predictors (?neuromarkers?) of alcohol use behaviors in youth. Identification of brain-based predictors of alcohol use behaviors in youth is essential for the development of more effective early prevention and intervention efforts. This proposal combines machine learning and longitudinal modeling approaches to 1) identify neural networks predictive of early alcohol initiation and misuse and 2) chart the developmental trajectories of these networks over time in a large sample of youth (N>3,000) using data from three unique, proprietary and completed datasets. Neural networks conferring vulnerability for alcohol use behaviors during adolescence will be identified using connectome-based predictive modeling (CPM). CPM is a machine-learning method of generating behavioral predictions from individual patterns of brain organization; i.e., functional connectivity matrices. Unlike traditional machine learning approaches, CPM is entirely data-driven and requires no a priori selection of brain regions or networks. As such, CPM is both a predictive tool and a method of identifying networks that underlie specific behaviors; i.e., neuromarkers. CPM has been successfully used to predict complex behaviors including future abstinence and other addiction-relevant phenotypes. This proposal will use CPM to identify neuromarkers of alcohol initiation and predict transitions to risky drinking in youth (AIM 1). Quantification of changes in brain function, e.g., growth curve trajectory analysis, is central to the characterization of developmental phenomena. Analyses of developmental trajectories can be used to identify particularly sensitive growth periods, detect variations that may signal risk, define modifiable targets, and monitor the impact of environment and interventions on development. While extant data indicate alcohol-related alterations in neural development, very few studies have assessed interactions between neurodevelopmental trajectories over time and alcohol-use behaviors. Developmental trajectories of identified networks in relation to alcohol use behaviors over time will be assessed using multilevel modeling (AIM 2). This proposal represents the first attempt to identify neural networks predictive of alcohol-initiation and risky drinking using a wholly data- driven, machine learning approach in a large sample of youth and does so using existing data. This is a critical step toward identifying a reliable predictor of alcohol initiation in youth and will shed light on individual difference factors representing vulnerability for misuse. Such predictors are needed to understand the developmental trajectories of alcohol phenotypes and to inform early risk models and preventative intervention efforts.